log2 fold change (MLE): cell line P1E10 vs Parental
Wald test p-value: cell line P1E10 vs Parental
DataFrame with 56739 rows and 6 columns
baseMean log2FoldChange lfcSE
<numeric> <numeric> <numeric>
GAS5 3358381 0.1558718 0.0336348
SNORD30 1605167 0.0910242 0.0577644
SNORD49A 1156164 0.1842127 0.0437422
SNORD26 857547 0.0954226 0.0541020
SNHG1 780897 -0.0217632 0.0401304
... ... ... ...
PJY302_EMX1:inactive_other 0 NA NA
PJY306_EMX1:active_cis 0 NA NA
PJY306_EMX1:active_trans 0 NA NA
PJY306_EMX1:inactive_cryptic_terminiation 0 NA NA
PJY306_EMX1:inactive_other 0 NA NA
stat pvalue padj
<numeric> <numeric> <numeric>
GAS5 4.634240 3.58252e-06 8.13534e-05
SNORD30 1.575783 1.15076e-01 4.53193e-01
SNORD49A 4.211322 2.53881e-05 4.97130e-04
SNORD26 1.763753 7.77736e-02 3.62974e-01
SNHG1 -0.542311 5.87604e-01 8.80000e-01
... ... ... ...
PJY302_EMX1:inactive_other NA NA NA
PJY306_EMX1:active_cis NA NA NA
PJY306_EMX1:active_trans NA NA NA
PJY306_EMX1:inactive_cryptic_terminiation NA NA NA
PJY306_EMX1:inactive_other NA NA NA
Code
summary(res_batch2_day1)
out of 51623 with nonzero total read count
adjusted p-value < 0.05
LFC > 0 (up) : 936, 1.8%
LFC < 0 (down) : 1750, 3.4%
outliers [1] : 1, 0.0019%
low counts [2] : 23895, 46%
(mean count < 1)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
using 'apeglm' for LFC shrinkage. If used in published research, please cite:
Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
sequence count data: removing the noise and preserving large differences.
Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895
log2 fold change (MLE): cell line P1E10 vs Parental
Wald test p-value: cell line P1E10 vs Parental
DataFrame with 56739 rows and 6 columns
baseMean log2FoldChange lfcSE
<numeric> <numeric> <numeric>
GAS5 3192415 0.18567762 0.0303372
SNORD30 1514330 0.15582664 0.0512420
SNORD49A 1170290 0.19302856 0.0428279
SNORD26 825436 0.09512922 0.0398450
SNHG1 678111 0.00437696 0.0311644
... ... ... ...
PJY302_EMX1:inactive_other 0 NA NA
PJY306_EMX1:active_cis 0 NA NA
PJY306_EMX1:active_trans 0 NA NA
PJY306_EMX1:inactive_cryptic_terminiation 0 NA NA
PJY306_EMX1:inactive_other 0 NA NA
stat pvalue padj
<numeric> <numeric> <numeric>
GAS5 6.120450 9.33112e-10 3.29420e-08
SNORD30 3.040992 2.35800e-03 2.71635e-02
SNORD49A 4.507080 6.57259e-06 1.38019e-04
SNORD26 2.387479 1.69644e-02 1.31205e-01
SNHG1 0.140447 8.88307e-01 9.76140e-01
... ... ... ...
PJY302_EMX1:inactive_other NA NA NA
PJY306_EMX1:active_cis NA NA NA
PJY306_EMX1:active_trans NA NA NA
PJY306_EMX1:inactive_cryptic_terminiation NA NA NA
PJY306_EMX1:inactive_other NA NA NA
Code
summary(res_batch2_day2)
out of 50456 with nonzero total read count
adjusted p-value < 0.05
LFC > 0 (up) : 879, 1.7%
LFC < 0 (down) : 1598, 3.2%
outliers [1] : 53, 0.11%
low counts [2] : 25267, 50%
(mean count < 1)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
using 'apeglm' for LFC shrinkage. If used in published research, please cite:
Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
sequence count data: removing the noise and preserving large differences.
Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895
using 'apeglm' for LFC shrinkage. If used in published research, please cite:
Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
sequence count data: removing the noise and preserving large differences.
Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895
using 'apeglm' for LFC shrinkage. If used in published research, please cite:
Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
sequence count data: removing the noise and preserving large differences.
Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895